|
Discrimination of Partial Discharge Sources in High-Voltage Cables Using Wavelet-Based Intelligent Algorithms.
|
Omid Sabarshad *1 , Asghar Akbari1  |
| 1- Department of Electrical Engineering, Faculty of Engineering and Technology, K. N. Toosi University of Technology, Tehran, Iran |
|
|
Abstract: (2166 Views) |
In this study, a comprehensive and cost-effective approach is proposed for classifying and analyzing various partial discharge (PD) scenarios in high-voltage power cables, aiming to enhance system efficiency. The methodology integrates signal processing techniques, extraction of physical-statistical features, and machine learning algorithms. To generate training data, detailed modeling of the cable structure was performed using COMSOL software, simulating diverse scenarios including: a healthy integrated cable, presence of a joint along the cable, single-source PDs (1 to 4 cavities), and combined cases involving joints with multiple cavity defects generating discharges. Subsequently, by analyzing the cable’s transient response under different discontinuities, the reflective signal patterns were characterized and fed into a machine learning model to identify the impact pattern of each scenario. The proposed model was then trained and evaluated in terms of classification accuracy, stability, and generalization capability. Overall, the results demonstrate that the combination of reflective signal analysis with time-frequency indicators and a support vector machine (SVM) algorithm provides a precise, robust, and interpretable framework for PD scenario classification. This method is suitable for deployment in power cable condition monitoring systems, even under noisy environments or limited data conditions. |
|
| Keywords: Partial Discharge, High-Voltage Cable, Discrete Wavelet Transform (DWT), Machine Learning, Time-Frequency Analysis, Support Vector Machine (SVM), Feature Extraction. |
|
|
Full-Text [PDF 1388 kb]
(1105 Downloads)
|
Type of Study: Research |
Received: 2025/07/31 | Accepted: 2025/12/3 | Published: 2025/12/27
|
|
|
|
|
|
|
| References |
1. Alqtish, M., Di Fatta, A., Rizzo, G., Akbar, G., Vigni, L., Imburgia, A., . . . Romano, P. (2025). A Review of Partial Discharge Electrical Localization Techniques in Power Cables: Practical Approaches and Circuit Models. Energies. doi:10.3390/en18102583 [ DOI:10.3390/en18102583] 2. Balouji, E., Hammarström, T., & McKelvey, T. (2022). Classification of Partial Discharges Originating From Multilevel PWM Using Machine Learning. IEEE Transactions on Dielectrics and Electrical Insulation, 29, 287-294. doi:10.1109/tdei.2022.3148461 [ DOI:10.1109/TDEI.2022.3148461] 3. Banjare, H. K., Sahoo, R., & Karmakar, S. (2022). Study and Analysis of Various Partial Discharge Signals Classification Using Machine Learning Application. 2022 IEEE 6th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), 52-56. doi:10.1109/CATCON56237.2022.10077703 [ DOI:10.1109/CATCON56237.2022.10077703] 4. Carvalho, I., Da Costa, E. G., Nobrega, L., & Silva, A. (2024). Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System. Sensors (Basel, Switzerland), 24. doi:10.3390/s24072226 [ DOI:10.3390/s24072226] 5. Chan, J. Q., Raymond, W., Illias, H., & Othman, M. (2023). Partial Discharge Localization Techniques: A Review of Recent Progress. Energies. doi:10.3390/en16062863 [ DOI:10.3390/en16062863] 6. Chang, C.-K., & Chang, H.-H. (2023). Learning Entirely Unknown Classes in Time-Series Data Using Convolutional Neural Networks for Insulation Status Assessment of Partial Discharges in Power Cable Joints. IEEE Transactions on Dielectrics and Electrical Insulation, 30, 2854-2861. doi:10.1109/TDEI.2023.3280441 [ DOI:10.1109/TDEI.2023.3280441] 7. Chen, W., Yang, Z., Song, J., Zhou, L., Xiang, L., Wang, X., . . . Fan, X. (2024). A High-Resolution Defect Location Method for Medium-Voltage Cables Based on Gaussian Narrow-Band Envelope Signals and the S-Transform. Energies, 17(9), 2218. Retrieved from https://www.mdpi.com/1996-1073/17/9/2218. [ DOI:10.3390/en17092218] 8. C. M. (2024). RF Mudule user's guide. In: COMSOL. 9. Da Silva, M., De Araújo, O., De Oliveira, D., & Lopes, R. (2024). Evaluation of the Effects of Voids in Electrical Cables Using COMSOL Multiphysics Software. IEEE Transactions on Dielectrics and Electrical Insulation, 31, 2144-2150. doi:10.1109/TDEI.2024.3395232 [ DOI:10.1109/TDEI.2024.3395232] 10. Dhandapani, R. (2024). Application of Signal Processing Techniques in High Voltage Equipment Fault Detection Using Partial Discharge Signal. doi:10.59019/vtjd9568 [ DOI:10.59019/VTJD9568] 11. Florkowski, M. (2021). Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns. Energies. doi:10.3390/en14133886 [ DOI:10.3390/en14133886] 12. Haiba, A., & Halawa, M. (2024). Design of partial discharge measurement model for internal cavities in high voltage power cables. Journal of King Saud University - Engineering Sciences. doi:10.1016/j.jksues.2024.02.002 [ DOI:10.1016/j.jksues.2024.02.002] 13. Hassan, W., Shafiq, M., Hussain, G., Choudhary, M., & Palu, I. (2023). Investigating the progression of insulation degradation in power cable based on partial discharge measurements. Electric Power Systems Research. doi:10.1016/j.epsr.2023.109452 [ DOI:10.1016/j.epsr.2023.109452] 14. Ishaq, A., Junaid, M., Hussain, G. A., Khan, S. U., Chen, Y., & Yu, D. (2025). Partial Discharge Defect Classification in MV Switchgear by Using CWT and Deep Learning Approach. IEEE Transactions on Instrumentation and Measurement, 74, 1-12. doi:10.1109/TIM.2025.3562981 [ DOI:10.1109/TIM.2025.3562981] 15. Janani, H., Shahabi, S., & Kordi, B. (2020). Separation and Classification of Concurrent Partial Discharge Signals Using Statistical-Based Feature Analysis. IEEE Transactions on Dielectrics and Electrical Insulation, 27, 1933-1941. doi:10.1109/TDEI.2020.009043 [ DOI:10.1109/TDEI.2020.009043] 16. Kim, J., & Kim, K.-I. (2021). Partial Discharge Online Detection for Long-Term Operational Sustainability of On-Site Low Voltage Distribution Network Using CNN Transfer Learning. Sustainability, 13, 4692. doi:10.3390/SU13094692 [ DOI:10.3390/su13094692] 17. Kumar, H., Shafiq, M., Kauhaniemi, K., & Elmusrati, M. (2024). A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques. Energies. doi:10.3390/en17051142 [ DOI:10.3390/en17051142] 18. Li, A., Wei, G., Li, S., Zhang, J., & Zhang, C.-Y. (2024). Pattern Recognition of Partial Discharge in High-Voltage Cables Using TFMT Model. IEEE Transactions on Power Delivery, 39, 3326-3337. doi:10.1109/TPWRD.2024.3465660 [ DOI:10.1109/TPWRD.2024.3465660] 19. Li, X., Ding, D., Xu, Y., Jiang, J., Chen, X., & Yuan, M. (2024). A Denoising Method for Partial Discharge Ultrasonic Signals in GIS Based on Ultra-High Frequency Signal Synchronization. IEEE Transactions on Power Delivery, 39, 3316-3325. doi:10.1109/TPWRD.2024.3465506 [ DOI:10.1109/TPWRD.2024.3465506] 20. Lu, S., Chai, H., Sahoo, A., & Phung, B. (2020). Condition Monitoring Based on Partial Discharge Diagnostics Using Machine Learning Methods: A Comprehensive State-of-the-Art Review. IEEE Transactions on Dielectrics and Electrical Insulation, 27, 1861-1888. doi:10.1109/TDEI.2020.009070 [ DOI:10.1109/TDEI.2020.009070] 21. Michau, G., Hsu, C.-C., & Fink, O. (2021). Interpretable Detection of Partial Discharge in Power Lines with Deep Learning. Sensors (Basel, Switzerland), 21. doi:10.3390/s21062154 [ DOI:10.3390/s21062154] 22. Qin, C., Zhu, X., Zhu, P., Lin, W., Liu, L., Che, C., . . . Hua, H. (2024). Partial Discharge Signal Pattern Recognition of Composite Insulation Defects in Cross-Linked Polyethylene Cables. Sensors (Basel, Switzerland), 24. doi:10.3390/s24113460 [ DOI:10.3390/s24113460] 23. Rathod, V., Kumbhar, G., & Bhalja, B. (2020). Simulation of Partial Discharge Acoustic Wave Propagation Using COMSOL Multiphysics and Its Localization in a Model Transformer Tank. 2020 21st National Power Systems Conference (NPSC), 1-6. doi:10.1109/NPSC49263.2020.9331915 [ DOI:10.1109/NPSC49263.2020.9331915] 24. Saad, M. H., Hashima, S., Omar, A. I., Fouda, M. M., & Said, A. (2025). Deep learning approach for cable partial discharge pattern identification. Electrical Engineering, 107(2), 1525-1540. doi:10.1007/s00202-024-02571-w [ DOI:10.1007/s00202-024-02571-w] 25. Sabarshad, O., & Akbari, A. (2025). Advanced detection of multiple PD sources in cables using time-frequency transformations. Electric Power Systems Research, 246, 111699. doi: [ DOI:10.1016/j.epsr.2025.111699] 26. Sahoo, R., & Karmakar, S. (2024). Effectiveness of Wavelet Scalogram on Partial Discharge Pattern Classification of XLPE Cable Insulation. IEEE Transactions on Instrumentation and Measurement, 73, 1-10. doi:10.1109/TIM.2024.3363790 [ DOI:10.1109/TIM.2024.3363790] 27. Sun, C., Wu, G., Pan, G., Zhang, T., Li, J., Jiao, S., . . . Gao, G. (2024). Convolutional Neural Network-Based Pattern Recognition of Partial Discharge in High-Speed Electric-Multiple-Unit Cable Termination. Sensors (Basel, Switzerland), 24. doi:10.3390/s24082660 [ DOI:10.3390/s24082660] 28. Tian, J., Song, H., Sheng, G., & Jiang, X. (2022). Knowledge-Driven Recognition Methodology of Partial Discharge Patterns in GIS. IEEE Transactions on Power Delivery, 37, 3335-3344. doi:10.1109/tpwrd.2021.3128036 [ DOI:10.1109/TPWRD.2021.3128036] 29. Wang, Y.-B., Chang, D.-G., Qin, S.-R., Fan, Y.-H., Mu, H., & Zhang, G. (2020). Separating Multi-Source Partial Discharge Signals Using Linear Prediction Analysis and Isolation Forest Algorithm. IEEE Transactions on Instrumentation and Measurement, 69, 2734-2742. doi:10.1109/tim.2019.2926688 [ DOI:10.1109/TIM.2019.2926688] 30. Zhang, X., Pang, B., Liu, Y., Liu, S.-Y., Xu, P., Li, Y., . . . Xie, Q. (2021). Review on Detection and Analysis of Partial Discharge along Power Cables. Energies. doi:10.3390/en14227692 [ DOI:10.3390/en14227692] 31. Zhong, J., Bi, X., Shu, Q., Chen, M., Zhou, D., & Zhang, D. (2020). Partial Discharge Signal Denoising Based on Singular Value Decomposition and Empirical Wavelet Transform. IEEE Transactions on Instrumentation and Measurement, 69, 8866-8873. doi:10.1109/TIM.2020.2996717 [ DOI:10.1109/TIM.2020.2996717]
|
|
Sabarshad O, Akbari A. Discrimination of Partial Discharge Sources in High-Voltage Cables Using Wavelet-Based Intelligent Algorithms.. ieijqp 2025; 14 (4) :1-15 URL: http://ieijqp.ir/article-1-1043-en.html
|